The impact of nut consumption on vascular endothelial function: a GRADE-assessed systematic review and meta-analysis of data from randomised controlled trials
Bibliographic record
Abstract
This meta-analysis aimed to evaluate the effect of nut consumption on vascular endothelial function through the conduction of a comprehensive review of randomised controlled trials. We explored the major electronic databases for published RCTs examining the repercussions of nuts consumption on vascular endothelial function indicators in adults (>18 years). We used random-effects models to compute pooled estimates of weighted mean differences and confidence intervals. The protocol of the present study was registered in the international database of systematic review protocols (CRD42023472892). Nineteen articles, comprising 21 arms, were deemed eligible. According to the pooled estimations, eating nuts significantly improved flow-mediated dilation (FMD) (weighted mean difference (WMD): 1.12%, 95% CI 0.28 to 1.97, p < 0.05), and reactive hyperaemia index (RHI) (WMD: −0.04, 95% CI −0.07 to −0.00, p = 0.04). However, findings revealed that consuming nuts had no significant impact on pulse wave velocity (PWV), the index of augmentation (AIx), or heart rate. The endothelial function was considerably enhanced by nut consumption through the improvement of FMD, while the certainty of such evidence was assessed as very low.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".